New parameterized model for GPS water vapor tomography

Abstract

Abstract. Water vapor is the basic parameter used to describe atmospheric conditions. It is rarely contained in the atmosphere during the water cycle, but it is the most active element in rapid space-time changes. Measuring and monitoring the distribution and quantity of water vapor is a necessary task. GPS tomography is a powerful means of providing high spatiotemporal resolution of water vapor density. In this paper, a spatial structure model of a humidity field is constructed using voxel nodes, and new parameterizations for acquiring data about water vapor in the troposphere via GPS are proposed based on inverse distance weighted (IDW) interpolation. Unlike the density of water vapor that is constant within a voxel, the density at a certain point is determined by IDW interpolation. This algorithm avoids the use of horizontal constraints to smooth voxels that are not crossed by satellite rays. A prime number decomposition (PND) access order scheme is introduced to minimize correlation between slant wet delay (SWD) observations. Four experimental schemes for GPS tomography are carried out in dry weather from 2 to 8 August 2015 and rainy days from 9 to 15 August 2015. Using 14 days of data from the Hong Kong Satellite Positioning Reference Station Network (SatRef), the results indicate that water vapor density derived from 4-node methods is more robust than that derived from that of 8 nodes or 12 nodes, or that derived from constant refractivity schemes and the new method has better performance under stable weather conditions than unstable weather (e.g., rainy days). The results also indicate that an excessive number of interpolations in each layer reduce accuracy. However, the accuracy of the tomography results is gradually reduced with increases in altitude below 7000 m. Moreover, in the case of altitudes between 7000 m and the upper boundary layer, the accuracy can be improved by a boundary constraint.

Adaptive Node Parameterization for Dynamic Determination of Boundaries and Nodes of GNSS Tomographic Models

“…The general node parameterization (Ding et al, ; Perler et al, ) and the adaptive node parameterization approach proposed in this study for tomographic modeling were tested with nonuniform vertical layers for the following two case studies: RNP: general node parameterization with a fixed cubic tomographic region; and ANP: adaptive node parameterization with an adaptive tomographic region. …”

“…The general node parameterization (Ding et al, 2017;Perler et al, 2011) and the adaptive node parameterization approach proposed in this study for tomographic modeling were tested with nonuniform vertical layers for the following two case studies:…”

A new approach for GNSS tomography from a few GNSS stations

“…This is different from the commonly used observation equation system in which the order of the observation equations is not a matter. In this study, an access order scheme based on prime number decomposition (PND), proposed in Ding et al (2017), was used for the ordering of the observation equations such that the observation equations between two consecutive iterations are largely uncorrelated. The unknown parameters X solved from Eq.…”

“…Yao et al (2016) improved the utilization rate of GNSS observations in the modeling by adding extra voxels on the top of the tomographic region where some satellite signals partly cross the tomographic field. Ding et al (2017) developed an access order scheme called prime number decomposition (PND) for minimizing the correlation between the SWDs which are the sample data of tomographic modeling. The above GNSS tomographic approaches were tested using various numbers of GNSS stations, the majority of which were a few dozen stations, and the maximum and minimum were 270 and 8, respectively.…”

“…The size and location of the rectangular cubes are determined based on the distribution of GNSS signals only on the top boundary of the tomographic field -the rectangular cube that best fits the top boundary is adopted (Bastin et al, 2005;Bender et al, 2009;Champollion et al, 2005;Ding et al, 2017;Gradinarsky and Jarlemark, 2004;Hoyle, 2005;Rohm et al, 2014;Seko et al, 2000;Troller et al, 2006;Xia et al, 2013;Ye et al, 2016). In fact, the field that GNSS signals cover has roughly the shape of an upside-down cone, meaning that in the part near the edge of the cube, especially in the lower part, none of the GNSS signals cross through.…”